Overview

Dataset statistics

Number of variables27
Number of observations10000
Missing cells60000
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory216.0 B

Variable types

Numeric11
Categorical10
Unsupported6

Alerts

UPN has a high cardinality: 8412 distinct valuesHigh cardinality
EntryDate has a high cardinality: 349 distinct valuesHigh cardinality
EnrolStatus is highly imbalanced (60.2%)Imbalance
TermlySessionsUnauthorised is highly imbalanced (99.5%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
TermlySessionsAuthorised has 931 (9.3%) zerosZeros
T_Reason_I has 477 (4.8%) zerosZeros
T_Reason_M has 2072 (20.7%) zerosZeros
T_Reason_S has 205 (2.1%) zerosZeros
T_Reason_T has 1357 (13.6%) zerosZeros
T_Reason_E has 2533 (25.3%) zerosZeros
T_Reason_C has 1194 (11.9%) zerosZeros
T_Reason_G has 2126 (21.3%) zerosZeros
T_Reason_O has 631 (6.3%) zerosZeros

Reproduction

Analysis started2023-06-26 14:05:21.051249
Analysis finished2023-06-26 14:05:49.457987
Duration28.41 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9608
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189836.43
Minimum63468
Maximum294802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:49.554613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum63468
5-th percentile133015.7
Q1166906.5
median190318.5
Q3212875.75
95-th percentile245756.1
Maximum294802
Range231334
Interquartile range (IQR)45969.25

Descriptive statistics

Standard deviation34109.383
Coefficient of variation (CV)0.17967776
Kurtosis-0.10695731
Mean189836.43
Median Absolute Deviation (MAD)22949
Skewness-0.044341877
Sum1.8983643 × 109
Variance1.16345 × 109
MonotonicityNot monotonic
2023-06-26T15:05:49.721358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176687 3
 
< 0.1%
195025 3
 
< 0.1%
175694 3
 
< 0.1%
205235 3
 
< 0.1%
197677 3
 
< 0.1%
189690 3
 
< 0.1%
182937 3
 
< 0.1%
174146 3
 
< 0.1%
200043 3
 
< 0.1%
193191 3
 
< 0.1%
Other values (9598) 9970
99.7%
ValueCountFrequency (%)
63468 1
< 0.1%
70704 1
< 0.1%
76115 1
< 0.1%
78784 1
< 0.1%
83708 1
< 0.1%
83882 1
< 0.1%
84178 1
< 0.1%
84237 1
< 0.1%
85407 1
< 0.1%
85574 1
< 0.1%
ValueCountFrequency (%)
294802 1
< 0.1%
292853 1
< 0.1%
291037 1
< 0.1%
290822 1
< 0.1%
289214 1
< 0.1%
289071 1
< 0.1%
288634 1
< 0.1%
288460 1
< 0.1%
288296 1
< 0.1%
287037 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8412
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
298b355a-28be-47a9-845a-dfb7289b290e
 
5
f3637a11-29a2-48e6-8f87-ccf005381594
 
4
dcd9d4ce-11a7-4382-9155-82b78fb4fd98
 
4
2392d58d-b4bf-46c8-8722-9a560eab1477
 
4
0a12efef-0712-46f5-b38d-c188124bdc03
 
4
Other values (8407)
9979 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7002 ?
Unique (%)70.0%

Sample

1st row6e2aa259-ee06-416f-ad12-1036992061a8
2nd row2bd78167-f6d1-452e-9487-9012efa4dc69
3rd row2483d8ab-c310-4b5c-a86e-cd560c162143
4th rowc204de8e-8b5b-486e-b2c8-8a0110377512
5th row2d27fbb7-da63-4963-af83-fc2fe1f2380d

Common Values

ValueCountFrequency (%)
298b355a-28be-47a9-845a-dfb7289b290e 5
 
0.1%
f3637a11-29a2-48e6-8f87-ccf005381594 4
 
< 0.1%
dcd9d4ce-11a7-4382-9155-82b78fb4fd98 4
 
< 0.1%
2392d58d-b4bf-46c8-8722-9a560eab1477 4
 
< 0.1%
0a12efef-0712-46f5-b38d-c188124bdc03 4
 
< 0.1%
c96cbaec-5aa1-4d33-9863-e6f1a947a5de 4
 
< 0.1%
6063e23c-1a17-4221-8115-9635f5db490d 4
 
< 0.1%
71f269aa-5cea-490e-9a18-b3aa5723cb49 4
 
< 0.1%
b07253f9-6bc2-4123-a506-f18f173f7d25 4
 
< 0.1%
68f4f08e-9172-4119-82eb-55fbe1c30f2c 4
 
< 0.1%
Other values (8402) 9959
99.6%

Length

2023-06-26T15:05:50.017450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
298b355a-28be-47a9-845a-dfb7289b290e 5
 
< 0.1%
b07253f9-6bc2-4123-a506-f18f173f7d25 4
 
< 0.1%
f3637a11-29a2-48e6-8f87-ccf005381594 4
 
< 0.1%
ae472a43-6e16-4d34-b5c5-9dcfefd78ec9 4
 
< 0.1%
b0efe191-1e80-41be-9499-d50d35ba63d1 4
 
< 0.1%
ab3f6770-5913-4600-902e-384e226cd550 4
 
< 0.1%
68f4f08e-9172-4119-82eb-55fbe1c30f2c 4
 
< 0.1%
c922e955-f2d2-4fc3-8c65-4ca94acab94d 4
 
< 0.1%
71f269aa-5cea-490e-9a18-b3aa5723cb49 4
 
< 0.1%
6063e23c-1a17-4221-8115-9635f5db490d 4
 
< 0.1%
Other values (8402) 9959
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28518
 
7.9%
a 21370
 
5.9%
8 21250
 
5.9%
b 21199
 
5.9%
9 21093
 
5.9%
1 18945
 
5.3%
f 18913
 
5.3%
d 18908
 
5.3%
2 18836
 
5.2%
Other values (7) 130968
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202160
56.2%
Lowercase Letter 117840
32.7%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28518
14.1%
8 21250
10.5%
9 21093
10.4%
1 18945
9.4%
2 18836
9.3%
3 18785
9.3%
5 18707
9.3%
0 18703
9.3%
6 18694
9.2%
7 18629
9.2%
Lowercase Letter
ValueCountFrequency (%)
a 21370
18.1%
b 21199
18.0%
f 18913
16.0%
d 18908
16.0%
c 18804
16.0%
e 18646
15.8%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242160
67.3%
Latin 117840
32.7%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.5%
4 28518
11.8%
8 21250
8.8%
9 21093
8.7%
1 18945
7.8%
2 18836
7.8%
3 18785
7.8%
5 18707
7.7%
0 18703
7.7%
6 18694
7.7%
Latin
ValueCountFrequency (%)
a 21370
18.1%
b 21199
18.0%
f 18913
16.0%
d 18908
16.0%
c 18804
16.0%
e 18646
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28518
 
7.9%
a 21370
 
5.9%
8 21250
 
5.9%
b 21199
 
5.9%
9 21093
 
5.9%
1 18945
 
5.3%
f 18913
 
5.3%
d 18908
 
5.3%
2 18836
 
5.2%
Other values (7) 130968
36.4%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
M
5070 
F
4930 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 5070
50.7%
F 4930
49.3%

Length

2023-06-26T15:05:50.261673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:50.542750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 5070
50.7%
f 4930
49.3%

Most occurring characters

ValueCountFrequency (%)
M 5070
50.7%
F 4930
49.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 5070
50.7%
F 4930
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5070
50.7%
F 4930
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5070
50.7%
F 4930
49.3%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
7975 
Leaver
1913 
M
 
96
S
 
16

Length

Max length6
Median length1
Mean length1.9565
Min length1

Characters and Unicode

Total characters19565
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowLeaver

Common Values

ValueCountFrequency (%)
C 7975
79.8%
Leaver 1913
 
19.1%
M 96
 
1.0%
S 16
 
0.2%

Length

2023-06-26T15:05:50.746365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:50.902606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 7975
79.8%
leaver 1913
 
19.1%
m 96
 
1.0%
s 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 7975
40.8%
e 3826
19.6%
L 1913
 
9.8%
a 1913
 
9.8%
v 1913
 
9.8%
r 1913
 
9.8%
M 96
 
0.5%
S 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
51.1%
Lowercase Letter 9565
48.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 7975
79.8%
L 1913
 
19.1%
M 96
 
1.0%
S 16
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
e 3826
40.0%
a 1913
20.0%
v 1913
20.0%
r 1913
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 7975
40.8%
e 3826
19.6%
L 1913
 
9.8%
a 1913
 
9.8%
v 1913
 
9.8%
r 1913
 
9.8%
M 96
 
0.5%
S 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 7975
40.8%
e 3826
19.6%
L 1913
 
9.8%
a 1913
 
9.8%
v 1913
 
9.8%
r 1913
 
9.8%
M 96
 
0.5%
S 16
 
0.1%

EntryDate
Categorical

Distinct349
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2010-09-02 00:00:00
757 
2011-09-07 00:00:00
719 
2014-09-04 00:00:00
702 
2013-09-05 00:00:00
657 
2014-09-03 00:00:00
653 
Other values (344)
6512 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)1.7%

Sample

1st row2014-09-04 00:00:00
2nd row2013-09-04 00:00:00
3rd row2014-07-15 00:00:00
4th row2014-09-05 00:00:00
5th row2010-09-01 00:00:00

Common Values

ValueCountFrequency (%)
2010-09-02 00:00:00 757
 
7.6%
2011-09-07 00:00:00 719
 
7.2%
2014-09-04 00:00:00 702
 
7.0%
2013-09-05 00:00:00 657
 
6.6%
2014-09-03 00:00:00 653
 
6.5%
2012-09-06 00:00:00 644
 
6.4%
2011-09-05 00:00:00 624
 
6.2%
2010-09-01 00:00:00 584
 
5.8%
2012-09-04 00:00:00 545
 
5.5%
2013-09-03 00:00:00 481
 
4.8%
Other values (339) 3634
36.3%

Length

2023-06-26T15:05:51.237337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2010-09-02 757
 
3.8%
2011-09-07 719
 
3.6%
2014-09-04 702
 
3.5%
2013-09-05 657
 
3.3%
2014-09-03 653
 
3.3%
2012-09-06 644
 
3.2%
2011-09-05 624
 
3.1%
2010-09-01 584
 
2.9%
2012-09-04 545
 
2.7%
Other values (340) 4115
20.6%

Most occurring characters

ValueCountFrequency (%)
0 91011
47.9%
- 20000
 
10.5%
: 20000
 
10.5%
1 14110
 
7.4%
2 13321
 
7.0%
10000
 
5.3%
9 9496
 
5.0%
4 4319
 
2.3%
3 3538
 
1.9%
5 2129
 
1.1%
Other values (3) 2076
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91011
65.0%
1 14110
 
10.1%
2 13321
 
9.5%
9 9496
 
6.8%
4 4319
 
3.1%
3 3538
 
2.5%
5 2129
 
1.5%
6 1166
 
0.8%
7 826
 
0.6%
8 84
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91011
47.9%
- 20000
 
10.5%
: 20000
 
10.5%
1 14110
 
7.4%
2 13321
 
7.0%
10000
 
5.3%
9 9496
 
5.0%
4 4319
 
2.3%
3 3538
 
1.9%
5 2129
 
1.1%
Other values (3) 2076
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91011
47.9%
- 20000
 
10.5%
: 20000
 
10.5%
1 14110
 
7.4%
2 13321
 
7.0%
10000
 
5.3%
9 9496
 
5.0%
4 4319
 
2.3%
3 3538
 
1.9%
5 2129
 
1.1%
Other values (3) 2076
 
1.1%

NCyearActual
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
9
2040 
11
1907 
8
1878 
10
1869 
12
1224 
Other values (3)
1082 

Length

Max length6
Median length2
Mean length1.8427
Min length1

Characters and Unicode

Total characters18427
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row7
3rd row11
4th row10
5th row10

Common Values

ValueCountFrequency (%)
9 2040
20.4%
11 1907
19.1%
8 1878
18.8%
10 1869
18.7%
12 1224
12.2%
Leaver 683
 
6.8%
7 387
 
3.9%
13 12
 
0.1%

Length

2023-06-26T15:05:51.533010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:52.153395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9 2040
20.4%
11 1907
19.1%
8 1878
18.8%
10 1869
18.7%
12 1224
12.2%
leaver 683
 
6.8%
7 387
 
3.9%
13 12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 6919
37.5%
9 2040
 
11.1%
8 1878
 
10.2%
0 1869
 
10.1%
e 1366
 
7.4%
2 1224
 
6.6%
L 683
 
3.7%
a 683
 
3.7%
v 683
 
3.7%
r 683
 
3.7%
Other values (2) 399
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14329
77.8%
Lowercase Letter 3415
 
18.5%
Uppercase Letter 683
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6919
48.3%
9 2040
 
14.2%
8 1878
 
13.1%
0 1869
 
13.0%
2 1224
 
8.5%
7 387
 
2.7%
3 12
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1366
40.0%
a 683
20.0%
v 683
20.0%
r 683
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14329
77.8%
Latin 4098
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6919
48.3%
9 2040
 
14.2%
8 1878
 
13.1%
0 1869
 
13.0%
2 1224
 
8.5%
7 387
 
2.7%
3 12
 
0.1%
Latin
ValueCountFrequency (%)
e 1366
33.3%
L 683
16.7%
a 683
16.7%
v 683
16.7%
r 683
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6919
37.5%
9 2040
 
11.1%
8 1878
 
10.2%
0 1869
 
10.1%
e 1366
 
7.4%
2 1224
 
6.6%
L 683
 
3.7%
a 683
 
3.7%
v 683
 
3.7%
r 683
 
3.7%
Other values (2) 399
 
2.2%

TermlySessionsPossible
Real number (ℝ)

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.0165
Minimum20
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:52.588566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile89
Q1110
median122
Q3132
95-th percentile138
Maximum140
Range120
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.875958
Coefficient of variation (CV)0.13339291
Kurtosis0.90483939
Mean119.0165
Median Absolute Deviation (MAD)10
Skewness-1.005553
Sum1190165
Variance252.04603
MonotonicityNot monotonic
2023-06-26T15:05:52.932755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134 314
 
3.1%
137 310
 
3.1%
138 308
 
3.1%
136 306
 
3.1%
127 299
 
3.0%
139 298
 
3.0%
130 292
 
2.9%
129 292
 
2.9%
135 286
 
2.9%
131 286
 
2.9%
Other values (83) 7009
70.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
50 1
 
< 0.1%
52 2
< 0.1%
53 2
< 0.1%
54 3
< 0.1%
55 4
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
140 146
1.5%
139 298
3.0%
138 308
3.1%
137 310
3.1%
136 306
3.1%
135 286
2.9%
134 314
3.1%
133 248
2.5%
132 285
2.9%
131 286
2.9%

TermlySessionsAuthorised
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3904
Minimum0
Maximum18
Zeros931
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:53.173548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6005507
Coefficient of variation (CV)0.76703361
Kurtosis0.85017931
Mean3.3904
Median Absolute Deviation (MAD)2
Skewness0.95869208
Sum33904
Variance6.7628641
MonotonicityNot monotonic
2023-06-26T15:05:53.434365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1887
18.9%
2 1605
16.1%
3 1432
14.3%
4 1213
12.1%
5 956
9.6%
0 931
9.3%
6 715
 
7.1%
7 487
 
4.9%
8 324
 
3.2%
9 191
 
1.9%
Other values (8) 259
 
2.6%
ValueCountFrequency (%)
0 931
9.3%
1 1887
18.9%
2 1605
16.1%
3 1432
14.3%
4 1213
12.1%
5 956
9.6%
6 715
 
7.1%
7 487
 
4.9%
8 324
 
3.2%
9 191
 
1.9%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 3
 
< 0.1%
15 1
 
< 0.1%
14 13
 
0.1%
13 15
 
0.1%
12 43
 
0.4%
11 65
 
0.7%
10 118
 
1.2%
9 191
1.9%
8 324
3.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

Length

2023-06-26T15:05:53.685235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:53.904792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

T_Reason_I
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6227
Minimum0
Maximum30
Zeros477
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:54.116254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile16
Maximum30
Range30
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9698103
Coefficient of variation (CV)0.75042057
Kurtosis0.83542543
Mean6.6227
Median Absolute Deviation (MAD)3
Skewness0.97970733
Sum66227
Variance24.699015
MonotonicityNot monotonic
2023-06-26T15:05:54.354729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 915
 
9.2%
2 910
 
9.1%
3 894
 
8.9%
4 894
 
8.9%
5 820
 
8.2%
6 734
 
7.3%
7 689
 
6.9%
8 601
 
6.0%
9 578
 
5.8%
0 477
 
4.8%
Other values (21) 2488
24.9%
ValueCountFrequency (%)
0 477
4.8%
1 915
9.2%
2 910
9.1%
3 894
8.9%
4 894
8.9%
5 820
8.2%
6 734
7.3%
7 689
6.9%
8 601
6.0%
9 578
5.8%
ValueCountFrequency (%)
30 2
 
< 0.1%
29 1
 
< 0.1%
28 3
 
< 0.1%
27 11
 
0.1%
26 6
 
0.1%
25 9
 
0.1%
24 14
 
0.1%
23 21
0.2%
22 24
0.2%
21 39
0.4%

T_Reason_M
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4773
Minimum0
Maximum7
Zeros2072
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:54.651978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1827192
Coefficient of variation (CV)0.80059511
Kurtosis0.83256316
Mean1.4773
Median Absolute Deviation (MAD)1
Skewness0.86133962
Sum14773
Variance1.3988246
MonotonicityNot monotonic
2023-06-26T15:05:54.850425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3675
36.8%
2 2472
24.7%
0 2072
20.7%
3 1181
 
11.8%
4 438
 
4.4%
5 124
 
1.2%
6 27
 
0.3%
7 11
 
0.1%
ValueCountFrequency (%)
0 2072
20.7%
1 3675
36.8%
2 2472
24.7%
3 1181
 
11.8%
4 438
 
4.4%
5 124
 
1.2%
6 27
 
0.3%
7 11
 
0.1%
ValueCountFrequency (%)
7 11
 
0.1%
6 27
 
0.3%
5 124
 
1.2%
4 438
 
4.4%
3 1181
 
11.8%
2 2472
24.7%
1 3675
36.8%
0 2072
20.7%

T_Reason_R
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8220 
1
1780 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8220
82.2%
1 1780
 
17.8%

Length

2023-06-26T15:05:55.067181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:55.347117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8220
82.2%
1 1780
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 8220
82.2%
1 1780
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8220
82.2%
1 1780
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8220
82.2%
1 1780
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8220
82.2%
1 1780
 
17.8%

T_Reason_S
Real number (ℝ)

Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0208
Minimum0
Maximum82
Zeros205
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:55.611276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median13
Q322
95-th percentile37
Maximum82
Range82
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.452558
Coefficient of variation (CV)0.7624466
Kurtosis0.90083209
Mean15.0208
Median Absolute Deviation (MAD)8
Skewness0.99692002
Sum150208
Variance131.16108
MonotonicityNot monotonic
2023-06-26T15:05:55.900521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 468
 
4.7%
4 432
 
4.3%
3 431
 
4.3%
2 429
 
4.3%
8 413
 
4.1%
5 389
 
3.9%
6 386
 
3.9%
9 380
 
3.8%
10 375
 
3.8%
7 363
 
3.6%
Other values (58) 5934
59.3%
ValueCountFrequency (%)
0 205
2.1%
1 468
4.7%
2 429
4.3%
3 431
4.3%
4 432
4.3%
5 389
3.9%
6 386
3.9%
7 363
3.6%
8 413
4.1%
9 380
3.8%
ValueCountFrequency (%)
82 1
 
< 0.1%
74 1
 
< 0.1%
69 1
 
< 0.1%
64 2
 
< 0.1%
63 3
< 0.1%
62 1
 
< 0.1%
61 2
 
< 0.1%
60 5
0.1%
59 4
< 0.1%
58 5
0.1%

T_Reason_T
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3009
Minimum0
Maximum13
Zeros1357
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:56.040502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7826601
Coefficient of variation (CV)0.77476643
Kurtosis0.89654488
Mean2.3009
Median Absolute Deviation (MAD)1
Skewness0.93235457
Sum23009
Variance3.177877
MonotonicityNot monotonic
2023-06-26T15:05:56.261502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 2599
26.0%
2 2151
21.5%
3 1620
16.2%
0 1357
13.6%
4 1078
10.8%
5 616
 
6.2%
6 343
 
3.4%
7 148
 
1.5%
8 54
 
0.5%
9 17
 
0.2%
Other values (3) 17
 
0.2%
ValueCountFrequency (%)
0 1357
13.6%
1 2599
26.0%
2 2151
21.5%
3 1620
16.2%
4 1078
10.8%
5 616
 
6.2%
6 343
 
3.4%
7 148
 
1.5%
8 54
 
0.5%
9 17
 
0.2%
ValueCountFrequency (%)
13 1
 
< 0.1%
11 4
 
< 0.1%
10 12
 
0.1%
9 17
 
0.2%
8 54
 
0.5%
7 148
 
1.5%
6 343
 
3.4%
5 616
 
6.2%
4 1078
10.8%
3 1620
16.2%

T_Reason_H
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
4780 
0
4151 
2
996 
3
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 4780
47.8%
0 4151
41.5%
2 996
 
10.0%
3 73
 
0.7%

Length

2023-06-26T15:05:56.511268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:56.726858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4780
47.8%
0 4151
41.5%
2 996
 
10.0%
3 73
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 4780
47.8%
0 4151
41.5%
2 996
 
10.0%
3 73
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4780
47.8%
0 4151
41.5%
2 996
 
10.0%
3 73
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4780
47.8%
0 4151
41.5%
2 996
 
10.0%
3 73
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4780
47.8%
0 4151
41.5%
2 996
 
10.0%
3 73
 
0.7%

T_Reason_E
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2106
Minimum0
Maximum6
Zeros2533
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:56.854162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.99787134
Coefficient of variation (CV)0.82427833
Kurtosis0.47518956
Mean1.2106
Median Absolute Deviation (MAD)1
Skewness0.75444169
Sum12106
Variance0.99574721
MonotonicityNot monotonic
2023-06-26T15:05:56.986170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 4138
41.4%
0 2533
25.3%
2 2282
22.8%
3 822
 
8.2%
4 190
 
1.9%
5 32
 
0.3%
6 3
 
< 0.1%
ValueCountFrequency (%)
0 2533
25.3%
1 4138
41.4%
2 2282
22.8%
3 822
 
8.2%
4 190
 
1.9%
5 32
 
0.3%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 32
 
0.3%
4 190
 
1.9%
3 822
 
8.2%
2 2282
22.8%
1 4138
41.4%
0 2533
25.3%

T_Reason_C
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6015
Minimum0
Maximum15
Zeros1194
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:57.147295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9943659
Coefficient of variation (CV)0.76662154
Kurtosis0.9258934
Mean2.6015
Median Absolute Deviation (MAD)1
Skewness0.94587794
Sum26015
Variance3.9774955
MonotonicityNot monotonic
2023-06-26T15:05:57.336023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 2329
23.3%
2 1973
19.7%
3 1634
16.3%
4 1234
12.3%
0 1194
11.9%
5 751
 
7.5%
6 428
 
4.3%
7 233
 
2.3%
8 128
 
1.3%
9 58
 
0.6%
Other values (4) 38
 
0.4%
ValueCountFrequency (%)
0 1194
11.9%
1 2329
23.3%
2 1973
19.7%
3 1634
16.3%
4 1234
12.3%
5 751
 
7.5%
6 428
 
4.3%
7 233
 
2.3%
8 128
 
1.3%
9 58
 
0.6%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 3
 
< 0.1%
11 11
 
0.1%
10 23
 
0.2%
9 58
 
0.6%
8 128
 
1.3%
7 233
 
2.3%
6 428
 
4.3%
5 751
7.5%
4 1234
12.3%

T_Reason_G
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4547
Minimum0
Maximum8
Zeros2126
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:57.618175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1655401
Coefficient of variation (CV)0.8012237
Kurtosis0.60133556
Mean1.4547
Median Absolute Deviation (MAD)1
Skewness0.80546694
Sum14547
Variance1.3584838
MonotonicityNot monotonic
2023-06-26T15:05:57.808405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3695
37.0%
2 2410
24.1%
0 2126
21.3%
3 1212
 
12.1%
4 421
 
4.2%
5 110
 
1.1%
6 21
 
0.2%
7 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 2126
21.3%
1 3695
37.0%
2 2410
24.1%
3 1212
 
12.1%
4 421
 
4.2%
5 110
 
1.1%
6 21
 
0.2%
7 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 4
 
< 0.1%
6 21
 
0.2%
5 110
 
1.1%
4 421
 
4.2%
3 1212
 
12.1%
2 2410
24.1%
1 3695
37.0%
0 2126
21.3%

T_Reason_U
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5230 
1
4477 
2
 
289
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5230
52.3%
1 4477
44.8%
2 289
 
2.9%
3 4
 
< 0.1%

Length

2023-06-26T15:05:57.987115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:58.134253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5230
52.3%
1 4477
44.8%
2 289
 
2.9%
3 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 5230
52.3%
1 4477
44.8%
2 289
 
2.9%
3 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5230
52.3%
1 4477
44.8%
2 289
 
2.9%
3 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5230
52.3%
1 4477
44.8%
2 289
 
2.9%
3 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5230
52.3%
1 4477
44.8%
2 289
 
2.9%
3 4
 
< 0.1%

T_Reason_O
Real number (ℝ)

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3646
Minimum0
Maximum25
Zeros631
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:05:58.304518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile13
Maximum25
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.064864
Coefficient of variation (CV)0.75771986
Kurtosis0.70277802
Mean5.3646
Median Absolute Deviation (MAD)3
Skewness0.9487828
Sum53646
Variance16.523119
MonotonicityNot monotonic
2023-06-26T15:05:58.544254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1136
11.4%
2 1111
11.1%
4 1045
10.4%
3 1043
10.4%
5 922
9.2%
6 800
8.0%
7 634
 
6.3%
0 631
 
6.3%
8 578
 
5.8%
9 491
 
4.9%
Other values (16) 1609
16.1%
ValueCountFrequency (%)
0 631
6.3%
1 1136
11.4%
2 1111
11.1%
3 1043
10.4%
4 1045
10.4%
5 922
9.2%
6 800
8.0%
7 634
6.3%
8 578
5.8%
9 491
4.9%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 3
 
< 0.1%
23 2
 
< 0.1%
22 2
 
< 0.1%
21 10
 
0.1%
20 16
 
0.2%
19 26
0.3%
18 33
0.3%
17 52
0.5%
16 61
0.6%

T_Reason_N
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8851 
1
1149 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8851
88.5%
1 1149
 
11.5%

Length

2023-06-26T15:05:58.766277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:05:58.969667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8851
88.5%
1 1149
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 8851
88.5%
1 1149
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8851
88.5%
1 1149
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8851
88.5%
1 1149
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8851
88.5%
1 1149
 
11.5%

Interactions

2023-06-26T15:05:46.367272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:23.262169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:25.432147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:27.748777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:30.115497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:32.373731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:34.536441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:36.923112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:39.017541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:41.254961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:43.932242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:46.548080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:23.463954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:25.624728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:27.904450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:30.296870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:32.573596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:34.735563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:37.051185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:39.228529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:41.686814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:44.123199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:46.734308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:23.668635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:25.805950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:28.121998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:30.542400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:32.817661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:34.944621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:37.189778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:39.461198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:42.120521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:44.399654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:46.925000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:23.893114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:25.983676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:28.335500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:30.942280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:33.041219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:35.170305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:37.375209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:39.702142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:42.326946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:44.621024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:47.131311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:24.070957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:26.130412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:28.590497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:31.068158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:33.222069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:35.446156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:37.590214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:39.900556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:42.591607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:44.804208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:47.339337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:24.288791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:26.441143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:28.822306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:31.222425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:33.393530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:35.740303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:37.788342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:40.103467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:42.814491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:44.998428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:47.520459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:24.483163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:26.665414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:28.968398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:31.391004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:33.573514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:35.913801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:37.955123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:40.302543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:43.029697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:45.195477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:47.719552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:24.703159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:26.865653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:29.136532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:31.595359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:33.752127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:36.063636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:38.152245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:40.518080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:43.165622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:45.371183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:47.890191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:24.895485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:27.053106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:29.381447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:31.757738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:33.945544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:36.240226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:38.376816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:40.726457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:43.318218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:45.628761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:48.089524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:25.051310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:27.286579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:29.686984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:31.957744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:34.095528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:36.520877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:38.599771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:40.894420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:43.508399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:45.958243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:48.249811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:25.218274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:27.490464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:29.921555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:32.183295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:34.267649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:36.765938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:38.835260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:41.052242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:43.756258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:05:46.190373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:05:59.116538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossibleTermlySessionsAuthorisedT_Reason_IT_Reason_MT_Reason_ST_Reason_TT_Reason_ET_Reason_CT_Reason_GT_Reason_OGenderEnrolStatusNCyearActualTermlySessionsUnauthorisedT_Reason_RT_Reason_HT_Reason_UT_Reason_N
Estab1.000-0.0910.0030.1020.086-0.0730.0270.0080.1020.023-0.0810.0150.0470.0000.0000.0000.0410.0030.078
TermlySessionsPossible-0.0911.000-0.0520.1420.045-0.479-0.015-0.036-0.0240.041-0.0010.0000.1240.1480.0000.0580.0000.0000.017
TermlySessionsAuthorised0.003-0.0521.000-0.180-0.104-0.077-0.020-0.032-0.071-0.027-0.0610.0900.0230.0030.0000.0350.0000.0000.000
T_Reason_I0.1020.142-0.1801.0000.211-0.1820.0070.0100.0590.0300.0940.0130.0280.0370.0350.0460.0000.0240.021
T_Reason_M0.0860.045-0.1040.2111.000-0.1000.0190.0240.0670.0310.0710.0370.0440.0000.0000.0310.0000.0210.006
T_Reason_S-0.073-0.479-0.077-0.182-0.1001.0000.007-0.040-0.045-0.078-0.0550.0240.1170.1910.0000.0500.0020.0110.000
T_Reason_T0.027-0.015-0.0200.0070.0190.0071.0000.0300.0090.0210.0190.0000.0000.0110.0000.0070.0000.0000.000
T_Reason_E0.008-0.036-0.0320.0100.024-0.0400.0301.0000.0780.0380.1370.0430.0000.0000.0000.0110.0000.0480.030
T_Reason_C0.102-0.024-0.0710.0590.067-0.0450.0090.0781.000-0.0050.1290.0000.0000.0000.0000.0000.0380.0260.032
T_Reason_G0.0230.041-0.0270.0300.031-0.0780.0210.038-0.0051.0000.0550.0110.0110.0000.0000.0000.0000.0630.000
T_Reason_O-0.081-0.001-0.0610.0940.071-0.0550.0190.1370.1290.0551.0000.0000.0430.0000.0000.0530.0000.1000.000
Gender0.0150.0000.0900.0130.0370.0240.0000.0430.0000.0110.0001.0000.0080.0000.0000.0000.0020.0150.013
EnrolStatus0.0470.1240.0230.0280.0440.1170.0000.0000.0000.0110.0430.0081.0000.1290.0000.0120.0300.0220.000
NCyearActual0.0000.1480.0030.0370.0000.1910.0110.0000.0000.0000.0000.0000.1291.0000.0420.0130.0290.0000.000
TermlySessionsUnauthorised0.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0421.0000.0000.0000.0000.000
T_Reason_R0.0000.0580.0350.0460.0310.0500.0070.0110.0000.0000.0530.0000.0120.0130.0001.0000.0000.0280.000
T_Reason_H0.0410.0000.0000.0000.0000.0020.0000.0000.0380.0000.0000.0020.0300.0290.0000.0001.0000.0140.000
T_Reason_U0.0030.0000.0000.0240.0210.0110.0000.0480.0260.0630.1000.0150.0220.0000.0000.0280.0141.0000.000
T_Reason_N0.0780.0170.0000.0210.0060.0000.0000.0300.0320.0000.0000.0130.0000.0000.0000.0000.0000.0001.000

Missing values

2023-06-26T15:05:48.613422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:05:49.200226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossibleTermlySessionsAuthorisedTermlySessionsUnauthorisedT_Reason_IT_Reason_MT_Reason_RT_Reason_ST_Reason_TT_Reason_HT_Reason_ET_Reason_CT_Reason_GT_Reason_UT_Reason_OT_Reason_N
01934286e2aa259-ee06-416f-ad12-1036992061a8NaNNaNNaNNaNNaNFNaNC2014-09-04 00:00:009109402201671245150
12544692bd78167-f6d1-452e-9487-9012efa4dc69NaNNaNNaNNaNNaNMNaNC2013-09-04 00:00:0071313017211202421140
22005842483d8ab-c310-4b5c-a86e-cd560c162143NaNNaNNaNNaNNaNFNaNC2014-07-15 00:00:0011138101921011211060
3202132c204de8e-8b5b-486e-b2c8-8a0110377512NaNNaNNaNNaNNaNFNaNC2014-09-05 00:00:0010122601100451140150
42272562d27fbb7-da63-4963-af83-fc2fe1f2380dNaNNaNNaNNaNNaNFNaNLeaver2010-09-01 00:00:00101051011202440133000
521990206a93aa2-6393-4433-bcb4-4a35dad7271cNaNNaNNaNNaNNaNMNaNC2012-09-04 00:00:00101272020211040351020
61872390050cee7-1b77-4e7a-abf7-d14f8d9d3a9bNaNNaNNaNNaNNaNFNaNC2014-09-03 00:00:00814060921211011060
719690170ae7fa7-d2e3-4d4b-9d68-4d385a066725NaNNaNNaNNaNNaNMNaNC2011-09-07 00:00:00111294018400503421100
81881478544b86f-1621-4c73-bef5-d630feb30541NaNNaNNaNNaNNaNMNaNLeaver2012-09-04 00:00:0012101601102200030040
9134367224ad917-6b39-4545-b75f-1104d246d6c5NaNNaNNaNNaNNaNMNaNC2011-09-07 00:00:00812000730430202010
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossibleTermlySessionsAuthorisedTermlySessionsUnauthorisedT_Reason_IT_Reason_MT_Reason_RT_Reason_ST_Reason_TT_Reason_HT_Reason_ET_Reason_CT_Reason_GT_Reason_UT_Reason_OT_Reason_N
9990239821f2b964e1-1324-42f7-8e40-db932ea2d87cNaNNaNNaNNaNNaNMNaNC2010-09-01 00:00:00Leaver1197010211030110010
99911763043caa4889-cc4d-460e-a54b-2b7aeffe9183NaNNaNNaNNaNNaNFNaNC2013-09-04 00:00:0011134301200560101070
999223892374591cdd-c71e-469a-9140-3ed912370194NaNNaNNaNNaNNaNMNaNC2013-09-03 00:00:001212950641521042141
9993223476c5d88065-c5d2-43d2-9f1f-522323190243NaNNaNNaNNaNNaNMNaNLeaver2014-09-03 00:00:0011138203201111160180
9994206152c254f397-d93f-49a1-8b81-4ba87178df21NaNNaNNaNNaNNaNFNaNM2014-10-22 00:00:00111062019101100132060
999521646429d97602-c91e-4032-a385-7047afb86cffNaNNaNNaNNaNNaNFNaNLeaver2011-09-05 00:00:00Leaver91006203351043040
999616661437e2186d-8db5-4581-8dab-b73491fc1b7aNaNNaNNaNNaNNaNMNaNC2013-09-06 00:00:00813730610051102130
9997222124af18045e-6ce3-4f40-aeb0-accdf9e1ac3eNaNNaNNaNNaNNaNFNaNC2012-04-01 00:00:001112610510531140150
99981941612ac891fb-26d6-4a39-a9eb-3cdb4e4fcf69NaNNaNNaNNaNNaNFNaNC2010-09-01 00:00:007110401111331111100
9999158013658d5595-6a3f-43ca-88ad-e9feb343db2dNaNNaNNaNNaNNaNMNaNC2013-09-02 00:00:00101284011101100010060